GM-019 Analysis of the categories of clinical trials in an oncological institute: glimpse into the future

Abstract

Background

Clinical trials represent the future scenario of new therapeutic opportunities and treatments and may be used to implement good governance and to formulate prospective budget impacting on the health sector. In this case, an analysis of clinical trials and trend treatments can represent a useful instrument for drug governance and definitions of budget provisions.

Purpose

The aim of this work was to create a database to identify trends in future treatments using clinical trial data. The database will be used in prospective budget analysis.

Material and methods

We used 4D software to extract clinical trial records of studies proposed to our Institute from 2013. After this we compiled an ex novo database containing all studies subdivided into: disease, drug type, association trials and/or comparison studies, phases of clinical trial and other information. Observational, surgical, diagnostic and non-pharmacological clinical trials were excluded. We analysed and summarised the data with Excel.

Results

The sponsors proposed 495 pharmacological interventional studies to our institute. The most represented disease was lung cancer (n=98), followed by breast cancer (n=92), gynaecological cancers (n=40) and haematological cancers (n=38). The types of drugs were distributed according to the following percentages: small molecules 31.9%, chemotherapy 27.7%, immunotherapy (including checkpoint inhibitors) 18.6%, antibodies 11.7%, hormonal therapy 6.4%, radiopharmaceuticals 1.0%, biosimilar drugs 1.4% and others 1.3%. 12.5% of the total studies were phase I and I/II (of which 43.6% were small molecules, 27.3% were immunotherapy). 29.5% of all clinical trials dealt with drug association, while 25.5% were comparison studies.

Conclusion

From this analysis we infer that there is evidence of a huge trend towards immunotherapy clinical trials with checkpoint inhibitor drugs. Most of the studies proposed were early phases and dealt with comparison of different treatments. The database created had been referenced in terms of budgets for different units of our institute. From this analysis we identified critical elements impacting future budgets. Through this work we are able to highlight some innovative spending indicators based on clinical trial analysis.